使用临床图像自动检测皮肤癌症:全面审查
1Computer Vision and Robotics Group, University of Girona, 17003 Girona, Spain.
Life (Basel, Switzerland)
|November 25, 2023
概括
在临床图像上使用机器学习进行早期皮肤癌检测至关重要. 本次审查强调了需要更好的临床数据集和模型,以分析随时间推移的形模式.
科学领域:
- 皮肤病学 皮肤病学
- 计算机科学 计算机科学
- 医疗成像医学成像
背景情况:
- 皮肤癌,特别是黑色素瘤,是一个日益关注的问题,需要早期检测.
- 机器学习 (ML) 对皮肤癌的识别有前途,但大多数研究都使用皮肤显微镜图像.
- 一般医生往往缺乏皮肤镜,依靠标准的临床图像进行诊断.
研究的目的:
- 综合审查使用临床图像检测皮肤癌的图像处理技术.
- 评估51篇最近的文章,重点是临床数据集中的皮肤癌检测ML方法.
主要方法:
- 51篇最先进的研究文章的系统审查和分析.
- 专注于利用机器学习用于从临床图像中检测皮肤癌的研究.
主要成果:
- 与皮肤透视数据集相比,只有少数公开的临床图像数据集可用于基准测试.
- 在ML模型中,当前的工件移除技术可能不足,并会对性能产生负面影响.
- 大多数研究都分析单个损伤图像,忽略了患者特定的形状和时间变化.
结论:
- 对于更大的标准化临床图像数据集,对于强大的ML模型开发有很大的需求.
- 未来的研究应该解决人工物移除的挑战,并纳入纵向数据,以提高皮肤癌检测准确度.
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